Reconstructing surface from a set of spatial curves is a fundamental problem in computer graphics and computational geometry. It often arises in many applications across various disciplines, such as industrial prototyping, artistic design and biomedical imaging. While the problem has been widely studied for years, challenges remain for handling different type of curve inputs while satisfying various constraints. We study studied three related computational tasks in this thesis. First, we propose an algorithm for reconstructing multi-labeled material interfaces from cross-sectional curves that allows for explicit topology control. Second, we addressed the consistency restoration, a critical but overlooked problem in applying algorithms of surface reconstruction to real-world cross-sections data. Lastly, we propose the Variational Implicit Point Set Surface which allows us to robustly handle noisy, sparse and non-uniform inputs, such as samples from spatial curves.
","abstract_html":"<p>Reconstructing surface from a set of spatial curves is a fundamental problem in computer graphics and computational geometry. It often arises in many applications across various disciplines, such as industrial prototyping, artistic design and biomedical imaging. While the problem has been widely studied for years, challenges remain for handling different type of curve inputs while satisfying various constraints. We study studied three related computational tasks in this thesis. First, we propose an algorithm for reconstructing multi-labeled material interfaces from cross-sectional curves that allows for explicit topology control. Second, we addressed the consistency restoration, a critical but overlooked problem in applying algorithms of surface reconstruction to real-world cross-sections data. Lastly, we propose the Variational Implicit Point Set Surface which allows us to robustly handle noisy, sparse and non-uniform inputs, such as samples from spatial curves.</p>","abstract_has_math":false,"creators":["Huang, Zhiyang"],"institution":null,"degree_name":"Doctor of Philosophy (PhD)","degree_level":"Dissertation","degree_discipline":"Computer Science & Engineering","degree_department":null,"school":null,"contributors":["Tao Ju","Nathan Carr, Ayan Chakrabarti, Ulugbek Kamilov, Caitlin Kelleher,"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2019,"date_issued":"2019-05-15T07:00:00Z","date_published":"2019-05-15T07:00:00Z","updated_at":"2026-07-24T06:13:05Z","subjects":["Computational Geometry","Curves","Surface Reconstruction","Computer Engineering","Computer Sciences"],"languages":["English (en)"],"rights":["I have not registered my thesis with the U.S. Copyright Office, but intend to later."],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["https://openscholarship.wustl.edu/eng_etds/448"],"render_values":[{"text":"https://openscholarship.wustl.edu/eng_etds/448","href":"https://openscholarship.wustl.edu/eng_etds/448","code":true}]}]},"links":{"outbound_url":"https://doi.org/7936/fz30-pq36","outbound_label":"DOI","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Tao Ju","Nathan Carr, Ayan Chakrabarti, Ulugbek Kamilov, Caitlin Kelleher,"]},{"key":"dc:creator","label":"Author","values":["Huang, Zhiyang"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2021-04-17T07:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science & Engineering","McKelvey School of Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctor of Philosophy (PhD)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Computational Geometry","Curves","Surface Reconstruction","Computer Engineering","Computer Sciences"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["English (en)"]},{"key":"dc:rights","label":"Dc Rights","values":["I have not registered my thesis with the U.S. Copyright Office, but intend to later."]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://doi.org/7936/fz30-pq36","https://openscholarship.wustl.edu/eng_etds/448"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Permanent URL: https://doi.org/7936/fz30-pq36"]},{"key":"dc:description.abstract","label":"Abstract","values":["Reconstructing surface from a set of spatial curves is a fundamental problem in computer graphics and computational geometry. It often arises in many applications across various disciplines, such as industrial prototyping, artistic design and biomedical imaging. While the problem has been widely studied for years, challenges remain for handling different type of curve inputs while satisfying various constraints. We study studied three related computational tasks in this thesis. First, we propose an algorithm for reconstructing multi-labeled material interfaces from cross-sectional curves that allows for explicit topology control. Second, we addressed the consistency restoration, a critical but overlooked problem in applying algorithms of surface reconstruction to real-world cross-sections data. Lastly, we propose the Variational Implicit Point Set Surface which allows us to robustly handle noisy, sparse and non-uniform inputs, such as samples from spatial curves.
"]},{"key":"dc:title","label":"Title","values":["Toward Controllable and Robust Surface Reconstruction from Spatial Curves"]}]}],"canonical_facts":{"dc:contributor":["Tao Ju","Nathan Carr, Ayan Chakrabarti, Ulugbek Kamilov, Caitlin Kelleher,"],"dc:creator":["Huang, Zhiyang"],"dc:date.available":["2021-04-17T07:00:00Z"],"dc:description":["Permanent URL: https://doi.org/7936/fz30-pq36"],"dc:description.abstract":["Reconstructing surface from a set of spatial curves is a fundamental problem in computer graphics and computational geometry. It often arises in many applications across various disciplines, such as industrial prototyping, artistic design and biomedical imaging. While the problem has been widely studied for years, challenges remain for handling different type of curve inputs while satisfying various constraints. We study studied three related computational tasks in this thesis. First, we propose an algorithm for reconstructing multi-labeled material interfaces from cross-sectional curves that allows for explicit topology control. Second, we addressed the consistency restoration, a critical but overlooked problem in applying algorithms of surface reconstruction to real-world cross-sections data. Lastly, we propose the Variational Implicit Point Set Surface which allows us to robustly handle noisy, sparse and non-uniform inputs, such as samples from spatial curves.
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